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Record W4214488621 · doi:10.21203/rs.3.rs-1383492/v1

The global spread of (mis)information on spiders

2022· preprint· en· W4214488621 on OpenAlexaff
Stefano Mammola, Jagoba Malumbres‐Olarte, Valeria Arabesky, Diego Alejandro Barrales-Alcalá, Aimee Lynn A. Barrion-Dupo, Marco Antonio Benamú, Tharina L. Bird, Maria Bogomolova, Pedro Cardoso, Maria Chatzaki, Ren‐Chung Cheng, Tien-Ai Chu, Leticia Classen‐Rodríguez, Iva Čupić, Naufal Urfi Dhiya’ulhaq, André‐Philippe Drapeau Picard, Hisham K. El-Hennawy, Mert Elverici, Caroline Sayuri Fukushima, Zeana Ganem, Efrat Gavish‐Regev, Naledi Troy Gonnye, Axel Hacala, Charles R. Haddad, Thomas Hesselberg, Tammy Ai Tian Ho, Thanakorn Into, Marco Isaia, Dharmaraj Jayaraman, Nanguei Karuaera, Rajashree Khalap, Kiran Khalap, Dongyoung Kim, Tuuli Korhonen, Simona Kralj‐Fišer, Heidi Land, Shou‐Wang Lin, Sarah Loboda, Elizabeth Lowe, Yael Lubin, Alejandro Martínez, Zingisile Mbo, Marija Miličić, Grace M. Kioko, Veronica Nanni, Yusoff Norma‐Rashid, Daniel Nwankwo, Christina J. Painting, Aleck Pang, Paolo Pantini, Martina Pavlek, Richard M. Pearce, Booppa Petcharad, Julien Pétillon, Onjaherizo Christian Raberahona, Philip Russo, Joni Saarinen, Laura Segura-Hernández, Lenka Sentenská, George R. Uhl, Leilani A. Walker, Charles M. Warui, Konrad Wiśniewski, Alireza Zamani, Angela Chuang, Catherine Scott

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsThe Scarborough HospitalMcGill UniversityUniversity of TorontoEspace pour la vie
Fundersnot available
KeywordsBusinessInternet privacyComputer science

Abstract

fetched live from OpenAlex

Abstract In the Internet era, the digital architecture that keeps us connected and informed may collaterally amplify the spread of misinformation and falsehood1,2. The magnitude of this problem is gaining global relevance3, as evidence accumulates that misinformation interferes with democratic processes and undermines collective responses to environmental and health crises4,5. Therefore, understanding how misinformation generates and spreads is becoming a pressing scientific, societal, and political challenge3. Advances in this area are delayed because high-resolution data on coherent information systems are difficult and time-consuming to acquire at global scales. We collated a high-resolution database of online newspaper articles on spider-human interactions. Spiders are widely feared animals6 that frequently appear in the spotlight of the global press7,8. Our database is unique in that it covers a global scale (5,348 news articles from 81 countries and 40 languages) while providing an expert-based assessment of the content and quality of each news article9. Here, we first show that the quality of news on spiders is exceedingly poor—47% of articles contained different types of error and 43% were sensationalistic—and we consolidate a quantitative understanding of the relationship between article quality and different news-level features. Among other factors, the consultancy of spider experts, but not doctors and other professionals, decrease sensationalism. Next, we show that the flow of spider-related information occurs within a highly interconnected global network and provide evidence that sensationalism, along with other predictors including numbers of spider species and internet users in a country, are key factors underlying the spread of information. Our results improve understanding of the drivers of (mis)information across broad-scale networks. They also represent a starting point to formulate recommendations for improving journalism quality. In the specific case of spiders, a more accurate media framing would translate into measurable benefits, limiting resource waste and mitigating human-wildlife conflicts and the prevalence of widespread arachnophobic sentiments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.590
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.117
GPT teacher head0.474
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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